Tomato (Solanum lycopersicum) is acknowledged worldwide as one of the major vegetable crops, as is the case in Iraq. This research in 2024 was designed to recognize the species of Root-Knot Nematode (RKN) in central Iraq, assess its distribution, and study the level of susceptibility of 5 tomato varieties (SPEEDY, GS-12, SUPER LUX, TALA, and JUDIVIS). Since there is a threat of chemical nematicides, a greenhouse study was performed to evaluate other control methods, such as the use of organic fertilizers and the extract of nettles (Urtica dioica). Root-knot nematode (RKN) species consisting of Meloidogyne incognita, M. arenaria, and M. javanica were found in Baghdad, while only M. incognita was found in Babil. Molecular identification confirmed M. javanica in both Baghdad and Babil. Among the tomato cultivars, GS-12, TALA, JUDIVIS, and SUPER LUX, the Root-Knot Nematode (RKN) galling index and the degree of susceptibility in root galling were significantly different. GS-12 was the most susceptible and had a galling index of 3.60, then TALA had a galling index of 3.20, and JUDIVIS had a galling index of 2.40. SUPER LUX had high resistance with an index of 2.20. GS-12 also had the most nematode reproduction with 144 juveniles + eggs/100 cm³ Soil, while TALA and SPEEDY had 81, which were much lower. Plant growth also differed in the varieties. TALA had the highest increase in stem height, which was 38.5 cm, while SUPER LUX had the highest fresh Weight of 7.0 g. The nematode-fungal infection of the plants was significantly lower in the alternative treatments. The most efficacious of the options provided was Urtica dioica (galling index 2.0), followed by sheep manure (2.2) and then cattle manure (2.4). Listed poultry manure was still less efficacious (3.2) and did not vary statistically from the control group (3.6). These results illustrate the potential for cultivar-specific targeting of RKNs and underscore the potential of both Urtica dioica and organic-based fertilizers for sustainable management of tomatoes. This study provided advances in the discipline of Plant Sciences, focusing on the integration of phytopathology and the science of protective agriculture.
A total of 50 fertile human hydatid cases {33(66%) females and (34%) males}, obtained from Al-Ramadi public Hospital during the period from December 2003 to July 2004 were examined to study any bacterial infections. The specimens were obtained from hydatid fluid and then cultured on appropriate culture media to distinguish some species of bacteria which resulted in obtaining: Staphylococcus aureus (18%), Pseudomonas aeruginosa(12%), Escherichia coli(6%) and Streptococcus pneumonia (4%). These bacteria were confirmed by isolation from interacyst fluid and blood culture technique. The possible routs of infection may be through blood, biliary ducts and bronchioles .The selectivity permeable of the cyst wall may be absent and that may allow
... Show MoreWorld statistics proved that the most of work dangerous accidents, which causes death, are occurred in the construction works. These accidents related to many causes such as loss of workers experience and ignoring rules of safety requirements, especially young workers. Due to the risk of accidents that may occur in the site of work, the idea of this study crystallized to show the relationship between the age of worker and number of injuries and accidents, to identify the causes of these injuries, and to put the appropriate solutions to avoid or reduce the risk of work injuries. Also, the research shows the main principles of safety requirements to forming a clear picture about the subject of the study. A questioner form was prepared to c
... Show MoreClassifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the networ
... Show MoreThe presence of hydrocarbons in the soil is considered one of the main problems of pollution. In our current study, eight samples isolated from soil saturated with hydrocarbons were taken from different areas of Baghdad, Iraq. In this study, 5 isolates belonging to Pseudomonas aeruginosa by 99%, 4 isolates to Klebsiella pneumoniae by 98%, and 3 isolates to Enterobacter hormaechei by 97% were diagnosed in different ways. A molecular examination was also conducted by 16sRNA. We recorded P. aeruginosa, K. Pneumoniae and E. hormaechei as new local isolates in NCBI. In addition, a comparison was made between our isolates and the global isolates to determine the degree of convergence in the evolutionary line. The genes alkB and nahAc7 were diagno
... Show MoreAbstract
For sparse system identification,recent suggested algorithms are
-norm Least Mean Square (
-LMS), Zero-Attracting LMS (ZA-LMS), Reweighted Zero-Attracting LMS (RZA-LMS), and p-norm LMS (p-LMS) algorithms, that have modified the cost function of the conventional LMS algorithm by adding a constraint of coefficients sparsity. And so, the proposed algorithms are named
-ZA-LMS,
In this research a new system identification algorithm is presented for obtaining an optimal set of mathematical models for system with perturbed coefficients, then this algorithm is applied practically by an “On Line System Identification Circuit”, based on real time speed response data of a permanent magnet DC motor. Such set of mathematical models represents the physical plant against all variation which may exist in its parameters, and forms a strong mathematical foundation for stability and performance analysis in control theory problems.
This study is concerned with organizational learning and its impact on total quality management in the education sector. Organizational learning is a process that provides the educational sector with the ability to adapt and respond rapidly to developments and changes in a better way according to its main dimensions (Mental Models, Personal Mastery, Team Learning, Shared Vision, System Thinking) by adopting the philosophy of Total Quality Management (TQM) in accordance with its basic dimensions (leadership, customer satisfaction, participation of workers, continuous improvement, training and education). The main purpose of this study is to know (the impact of the Senge model of organizational learni
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